hpge_data_treat.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# HPGe paper calibration\n",
    "# Done by: Sebastian Sarasti"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import modules\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import radioactivedecay as rd\n",
    "import calendar\n",
    "from d_funciones import tasa, netcount, incertidumbre, area, max_peak, spec_hist, spec_s_act,spec_s_f,gauss_fit,spectrum\n",
    "from datetime import datetime, date, time, timedelta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import experimental data\n",
    "am_e={'0cm':np.loadtxt('./e_results/Am-241/'+'Am241-0cm-CalibracionEficiencias22-10-2019.TKA'),\n",
    "      '5cm':np.loadtxt('./e_results/Am-241/'+'Am241-5cm-CalibracionEficiencias22-10-2019.TKA'),\n",
    "      '10cm':np.loadtxt('./e_results/Am-241/'+'Am241-10cm-CalibracionEficiencias-24-10-2019.TKA'),\n",
    "      '20cm':np.loadtxt('./e_results/Am-241/'+'Am241-20cm-CalibracionEficiencias-23-10-2019.TKA'),\n",
    "      '25cm':np.loadtxt('./e_results/Am-241/'+'Am241-25cm-CalibracionEficiencias-23-10-2019.TKA')\n",
    "      }\n",
    "co_e={'0cm':np.loadtxt('./e_results/Co-60/'+'Co60-0cm-CalibracionEficiencias-15-08-2019.TKA'),\n",
    "      '5cm':np.loadtxt('./e_results/Co-60/'+'Co60-5cm-CalibracionEficiencias-24-10-2019.TKA'),\n",
    "      '10cm':np.loadtxt('./e_results/Co-60/'+'Co60-10cmCalibracionEficiencias22ago2019.TKA'),\n",
    "      '20cm':np.loadtxt('./e_results/Co-60/'+'Co60-20cmCalibracionEficiencias23ago2019.TKA'),\n",
    "      '25cm':np.loadtxt('./e_results/Co-60/'+'Co60-25cmCalibracionEficiencias23ago2019.TKA')\n",
    "      }\n",
    "cs_e={'0cm':np.loadtxt('./e_results/Cs-137/'+'Cs137-0cm-CalibracionEficiencias-15-08-2019.TKA'),\n",
    "      '5cm':np.loadtxt('./e_results/Cs-137/'+'Cs137-5cmcalibracioneficiencia20ago2019.TKA'),\n",
    "      '10cm':np.loadtxt('./e_results/Cs-137/'+'Cs137-10cmCalibracionEficiencia22ago2019.TKA'),\n",
    "      '20cm':np.loadtxt('./e_results/Cs-137/'+'Cs137-20cmCalibracionEficiencias23ago2019.TKA'),\n",
    "      '25cm':np.loadtxt('./e_results/Cs-137/'+'Cs137-25cmCalibracionEficiencias27ago2019.TKA')\n",
    "      }\n",
    "eu_e={'0cm':np.loadtxt('./e_results/Eu-152/'+'Eu152-0cm-CalibracionEficiencias-15-09-2019.TKA'),\n",
    "      '5cm':np.loadtxt('./e_results/Eu-152/'+'Eu152-5cm-CalibracionEficiencias-19-08-2019.TKA'),\n",
    "      '10cm':np.loadtxt('./e_results/Eu-152/'+'Eu152-10cmCalibracionEficiencias22ago2019.TKA'),\n",
    "      '20cm':np.loadtxt('./e_results/Eu-152/'+'Eu152-20cmCalibracionEficiencias23ago2019.TKA'),\n",
    "      '25cm':np.loadtxt('./e_results/Eu-152/'+'Eu152-25cmCalibracionEficiencias27ago2019.TKA')\n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load background radiation\n",
    "bk=np.loadtxt(\"./e_results/\"+'afondo20min07nov2018.TKA')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determination of net rate count spectra\n",
    "# extract the keys of before dictionaries\n",
    "claves=list(eu_e.keys())\n",
    "# create an empty dictionary\n",
    "am_e_n={}\n",
    "cs_e_n={}\n",
    "co_e_n={}\n",
    "eu_e_n={}\n",
    "for clave in claves:\n",
    "    am_e_n[clave]=netcount(am_e[clave],bk)\n",
    "    cs_e_n[clave]=netcount(cs_e[clave],bk)\n",
    "    co_e_n[clave]=netcount(co_e[clave],bk)\n",
    "    eu_e_n[clave]=netcount(eu_e[clave],bk)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 360x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# make a linear regression\n",
    "ca=np.array([0,223,457,921,1297,2492,4420,5020])\n",
    "energia=np.array([0,0.0595,0.121,0.244,0.344,0.662,1.17,1.33])#MeV \n",
    "from scipy import stats\n",
    "res = stats.linregress(ca, energia)\n",
    "# save the constants like variables\n",
    "r2=res[2]\n",
    "m_=res[0]\n",
    "b_=res[1]\n",
    "\n",
    "    #%%\n",
    "\n",
    "# plot the linear regresion (channel calibration)\n",
    "textstr = '\\n'.join((\n",
    "    r'$R^2=%.2f$' % (0.99, ),\n",
    "    r'$b=%.5f$' % (b_, ),\n",
    "    r'$m=%.5f$' % (m_, )))\n",
    "fig2=plt.figure(figsize=(5,5))\n",
    "ax=plt.axes()\n",
    "trans_ca=lambda x:res[0]*x+res[1]\n",
    "plt.scatter(ca,energia,color='r',label='Points')\n",
    "plt.plot(ca,trans_ca(ca),color='b',label='Linear Regression')\n",
    "plt.grid()\n",
    "plt.xlabel('Channel')\n",
    "plt.ylabel('Energy (MeV)')\n",
    "plt.legend(loc=4)\n",
    "props = dict(boxstyle='round', facecolor='blue', alpha=0.1)\n",
    "ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=10,\n",
    "        verticalalignment='top', bbox=props)\n",
    "plt.savefig(\"channel_cab_hpge.png\",dpi=500)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "canal=np.arange(1,8191,1,dtype=int)\n",
    "# generate energy vector\n",
    "Energy=trans_ca(canal)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "outputs": [
    {
     "data": {
      "text/plain": "array([   1,    2,    3, ..., 8188, 8189, 8190])"
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "canal"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "outputs": [],
   "source": [
    "# generate a zero matrix to save the counts in each photopeak\n",
    "areas=np.zeros((7,5))\n",
    "a_am=np.array([area(am_e_n[clave],Energy,223-20,223+20) for clave in am_e_n])\n",
    "a_eu1=np.array([area(eu_e_n[clave],Energy,444,473) for clave in eu_e_n])\n",
    "a_eu2=np.array([area(eu_e_n[clave],Energy,921-20,921+20) for clave in eu_e_n])\n",
    "a_eu3=np.array([area(eu_e_n[clave],Energy,1297-20,1297+20) for clave in eu_e_n])\n",
    "a_cs=np.array([area(cs_e_n[clave],Energy,2492-20,2492+20) for clave in cs_e_n])\n",
    "a_co1=np.array([area(co_e_n[clave],Energy,4000,4500) for clave in co_e_n])\n",
    "a_co2=np.array([area(co_e_n[clave],Energy,4700,5250) for clave in co_e_n])\n",
    "areas[0,:]=a_am\n",
    "areas[1,:]=a_eu1\n",
    "areas[2,:]=a_eu2\n",
    "areas[3,:]=a_eu3\n",
    "areas[4,:]=a_cs\n",
    "areas[5,:]=a_co1\n",
    "areas[6,:]=a_co2"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "array([[8.88846361e-01, 1.04028385e-01, 3.80615756e-02, 1.85403600e-02,\n        7.25182872e-03],\n       [2.95867571e-01, 5.24590345e-02, 1.93661850e-02, 5.86205168e-03,\n        3.91494391e-03],\n       [4.02518328e-02, 7.67456284e-03, 3.04142705e-03, 9.70647860e-04,\n        6.68256124e-04],\n       [1.05154524e-01, 1.34296803e-02, 4.90359807e-03, 1.51201386e-03,\n        1.02027093e-03],\n       [4.85642305e-02, 5.42681063e-03, 1.92582759e-03, 5.79401767e-04,\n        3.87788295e-04],\n       [1.33962059e-01, 1.62418443e-02, 6.01531670e-03, 1.79853283e-03,\n        1.19821875e-03],\n       [6.33041768e-02, 6.79140829e-03, 2.48141102e-03, 7.36014238e-04,\n        5.07505332e-04]])"
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "outputs": [],
   "source": [
    "# create a vector of energies which are used for calibration\n",
    "energia_=np.array([0.0595,0.121,0.244,0.344,0.662,1.17,1.33])#MeV"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# add the probability of emission in each decay\n",
    "actf=np.array([37210.71,29118.16,8291.76,20030.49])\n",
    "am_t=actf[0]*0.357\n",
    "eu_t=actf[1]*0.2831\n",
    "eu_t_1=actf[1]*0.0749\n",
    "eu_t_2=actf[1]*0.266\n",
    "cs_t=actf[2]*0.851\n",
    "co_t_1=actf[3]*0.9988\n",
    "co_t_2=actf[3]*1\n",
    "# create a vector of each activity\n",
    "cuentas_t=[]\n",
    "cuentas_t.append(am_t)\n",
    "cuentas_t.append(eu_t)\n",
    "cuentas_t.append(eu_t_1)\n",
    "cuentas_t.append(eu_t_2)\n",
    "cuentas_t.append(cs_t)\n",
    "cuentas_t.append(co_t_1)\n",
    "cuentas_t.append(co_t_2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "outputs": [],
   "source": [
    "# determinate the new activity for Co-60 at 05-March-2020\n",
    "inv=rd.Inventory({'Co-60':20030.49}, 'Bq')\n",
    "ac_cof=inv.decay(131, 'd')\n",
    "# the result is 19107.73"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "eficiencia=np.zeros((7,5))\n",
    "# determinate FEPE\n",
    "for i in range(7):\n",
    "    for j in range(5):\n",
    "        eficiencia[i,j]=(areas[i,j]/cuentas_t[i])*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[8.88846361e-01, 1.04028385e-01, 3.80615756e-02, 1.85403600e-02,\n        7.25182872e-03],\n       [2.95867571e-01, 5.24590345e-02, 1.93661850e-02, 5.86205168e-03,\n        3.91494391e-03],\n       [4.02518328e-02, 7.67456284e-03, 3.04142705e-03, 9.70647860e-04,\n        6.68256124e-04],\n       [1.05154524e-01, 1.34296803e-02, 4.90359807e-03, 1.51201386e-03,\n        1.02027093e-03],\n       [4.85642305e-02, 5.42681063e-03, 1.92582759e-03, 5.79401767e-04,\n        3.87788295e-04],\n       [1.33962059e-01, 1.62418443e-02, 6.01531670e-03, 1.79853283e-03,\n        1.19821875e-03],\n       [6.33041768e-02, 6.79140829e-03, 2.48141102e-03, 7.36014238e-04,\n        5.07505332e-04]])"
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "areas"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(energia_,eficiencia[:,0],'o-', label='0 cm')\n",
    "plt.plot(energia_,eficiencia[:,1], 'o-', label='5 cm')\n",
    "plt.plot(energia_,eficiencia[:,2], 'o-', label='10 cm')\n",
    "plt.plot(energia_,eficiencia[:,3], 'o-', label='20 cm')\n",
    "plt.plot(energia_,eficiencia[:,4], 'o-', label='25 cm')\n",
    "plt.xlabel(\"Energy (MeV)\")\n",
    "plt.ylabel(\"FEPE (%)\")\n",
    "plt.grid()\n",
    "plt.legend()\n",
    "plt.yscale('log')\n",
    "plt.savefig('exp_cab_ener.png',dpi=500)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load experimetal data\n",
    "am_s={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_am_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_am_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_am_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_am_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_am_pos6.dat')\n",
    "      }\n",
    "co_s={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_co_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_co_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_co_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_co_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_co_pos6.dat')\n",
    "      }\n",
    "cs_s={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_cs_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_cs_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_cs_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_cs_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_cs_pos6.dat')\n",
    "      }\n",
    "eu_s1={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_eu1_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_eu1_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_eu1_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_eu1_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_eu1_pos6.dat')\n",
    "      }\n",
    "eu_s2={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_eu2_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_eu2_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_eu2_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_eu2_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_eu2_pos6.dat')\n",
    "      }\n",
    "eu_s3={'0cm':np.loadtxt('./s_results/'+'HPGeDetector_eu3_pos1.dat'),\n",
    "      '5cm':np.loadtxt('./s_results/'+'HPGeDetector_eu3_pos2.dat'),\n",
    "      '10cm':np.loadtxt('./s_results/'+'HPGeDetector_eu3_pos3.dat'),\n",
    "      '20cm':np.loadtxt('./s_results/'+'HPGeDetector_eu3_pos5.dat'),\n",
    "      '25cm':np.loadtxt('./s_results/'+'HPGeDetector_eu3_pos6.dat')\n",
    "      }"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a energy_vector transfor with just 1 element to use the function spec_hist\n",
    "Energy_=trans_ca(np.arange(1,8192,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create an empty dictionary to save the simulated transform spectra\n",
    "am_s_t={}\n",
    "cs_s_t={}\n",
    "co_s_t={}\n",
    "eu1_s_t={}\n",
    "eu2_s_t={}\n",
    "eu3_s_t={}\n",
    "for clave in claves:\n",
    "    am_s_t[clave]=spec_hist(am_s[clave],Energy_)\n",
    "    cs_s_t[clave]=spec_hist(cs_s[clave],Energy_)\n",
    "    co_s_t[clave]=spec_hist(co_s[clave],Energy_)\n",
    "    eu1_s_t[clave]=spec_hist(eu_s1[clave],Energy_)\n",
    "    eu2_s_t[clave]=spec_hist(eu_s2[clave],Energy_)\n",
    "    eu3_s_t[clave]=spec_hist(eu_s3[clave],Energy_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale simulated spectra to activity of each radioactive source\n",
    "cuentas_t\n",
    "# create dictionaries to save spectra with activity correction\n",
    "am_s_a={}\n",
    "cs_s_a={}\n",
    "co_s_a={}\n",
    "eu1_s_a={}\n",
    "eu2_s_a={}\n",
    "eu3_s_a={}\n",
    "for clave in claves:\n",
    "    am_s_a[clave]=spec_s_act(am_s_t[clave],1e6,cuentas_t[0])\n",
    "    eu1_s_a[clave]=spec_s_act(eu1_s_t[clave],1e6,cuentas_t[1])\n",
    "    eu2_s_a[clave]=spec_s_act(eu2_s_t[clave],1e6,cuentas_t[2])\n",
    "    eu3_s_a[clave]=spec_s_act(eu3_s_t[clave],1e6,cuentas_t[3])\n",
    "    cs_s_a[clave]=spec_s_act(cs_s_t[clave],1e6,cuentas_t[4])\n",
    "    co_s_a[clave]=spec_s_act(co_s_t[clave],1e6,cuentas_t[5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# determination of correction factor between each spectrum\n",
    "# extract the maximun of each experimental spectrum\n",
    "m_am=np.array([max(am_e_n[clave]) for clave in claves ])\n",
    "m_eu1=np.array([max(eu_e_n[clave][300:500]) for clave in claves ])\n",
    "m_eu2=np.array([max(eu_e_n[clave][750:1000]) for clave in claves ])\n",
    "m_eu3=np.array([max(eu_e_n[clave][1250:1400]) for clave in claves ])\n",
    "m_cs=np.array([max(cs_e_n[clave][2000:3000]) for clave in claves ])\n",
    "m_co1=np.array([max(co_e_n[clave][4000:4500]) for clave in claves ])\n",
    "m_co2=np.array([max(co_e_n[clave][4700:5250]) for clave in claves ])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# extract the maximun of each simulated spectrum\n",
    "m_am_s=np.array([max(am_s_a[clave][1]) for clave in claves ])\n",
    "m_eu1_s=np.array([max(eu1_s_a[clave][1][300:500]) for clave in claves ])\n",
    "m_eu2_s=np.array([max(eu2_s_a[clave][1][750:1000]) for clave in claves ])\n",
    "m_eu3_s=np.array([max(eu3_s_a[clave][1][1250:1400]) for clave in claves ])\n",
    "m_cs_s=np.array([max(cs_s_a[clave][1][2000:3000]) for clave in claves ])\n",
    "m_co1_s=np.array([max(co_s_a[clave][1][4200:4500]) for clave in claves ])\n",
    "m_co2_s=np.array([max(co_s_a[clave][1][4700:5250]) for clave in claves ])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# verify the maximum of each spectrum\n",
    "f_am=m_am_s/m_am\n",
    "f_eu1=m_eu1_s/m_eu1\n",
    "f_eu2=m_eu2_s/m_eu2\n",
    "f_eu3=m_eu3_s/m_eu3\n",
    "f_cs=m_cs_s/m_cs\n",
    "f_co1=m_co1_s/m_co1\n",
    "f_co2=m_co2_s/m_co2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2576.81384915  282.92005297   90.88294583   26.33750675   17.2780639 ] [278.15805556  50.05763889  18.22694444   5.47583333   3.65986111]\n"
     ]
    }
   ],
   "source": [
    "print(m_eu1_s,m_eu1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "factores=pd.DataFrame(index=['Am-241 59.5 keV','Eu-152 121 keV','Eu-152 244 keV',\n",
    "                             'Eu-152 344 keV','Cs-137 662 keV','Co-60 1173 keV','Co-60 1332 keV'], \n",
    "                      columns=['0 cm', '5 cm', '10 cm',  '20 cm', '25 cm'])\n",
    "factores.iloc[0,:]=f_am\n",
    "factores.iloc[1,:]=f_eu1\n",
    "factores.iloc[2,:]=f_eu2\n",
    "factores.iloc[3,:]=f_eu3\n",
    "factores.iloc[4,:]=f_cs\n",
    "factores.iloc[5,:]=f_co1\n",
    "factores.iloc[6,:]=f_co2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [],
   "source": [
    "# generate the mean\n",
    "factores['Mean']=factores.mean(axis='columns')"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "factores['Standard deviation']=factores.std(axis='columns')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "                      0 cm      5 cm     10 cm     20 cm     25 cm      Mean  \\\nAm-241 59.5 keV   5.140521  5.181251  4.348695  2.457052  4.147093  4.254922   \nEu-152 121 keV    9.263848  5.651886  4.986187  4.809771  4.720962  5.886531   \nEu-152 244 keV   13.930196  6.681265  6.016515  5.665935  5.598074  7.578397   \nEu-152 344 keV    9.680531  7.442181  6.695973  6.613153  6.248767  7.336121   \nCs-137 662 keV    9.608262  8.269331  7.791589  7.550638  7.662434  8.176451   \n\n                 Standard deviation  \nAm-241 59.5 keV            0.989457  \nEu-152 121 keV             1.719878  \nEu-152 244 keV             3.199027  \nEu-152 344 keV             1.234692  \nCs-137 662 keV             0.756661  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0 cm</th>\n      <th>5 cm</th>\n      <th>10 cm</th>\n      <th>20 cm</th>\n      <th>25 cm</th>\n      <th>Mean</th>\n      <th>Standard deviation</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Am-241 59.5 keV</th>\n      <td>5.140521</td>\n      <td>5.181251</td>\n      <td>4.348695</td>\n      <td>2.457052</td>\n      <td>4.147093</td>\n      <td>4.254922</td>\n      <td>0.989457</td>\n    </tr>\n    <tr>\n      <th>Eu-152 121 keV</th>\n      <td>9.263848</td>\n      <td>5.651886</td>\n      <td>4.986187</td>\n      <td>4.809771</td>\n      <td>4.720962</td>\n      <td>5.886531</td>\n      <td>1.719878</td>\n    </tr>\n    <tr>\n      <th>Eu-152 244 keV</th>\n      <td>13.930196</td>\n      <td>6.681265</td>\n      <td>6.016515</td>\n      <td>5.665935</td>\n      <td>5.598074</td>\n      <td>7.578397</td>\n      <td>3.199027</td>\n    </tr>\n    <tr>\n      <th>Eu-152 344 keV</th>\n      <td>9.680531</td>\n      <td>7.442181</td>\n      <td>6.695973</td>\n      <td>6.613153</td>\n      <td>6.248767</td>\n      <td>7.336121</td>\n      <td>1.234692</td>\n    </tr>\n    <tr>\n      <th>Cs-137 662 keV</th>\n      <td>9.608262</td>\n      <td>8.269331</td>\n      <td>7.791589</td>\n      <td>7.550638</td>\n      <td>7.662434</td>\n      <td>8.176451</td>\n      <td>0.756661</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "factores.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scale the simulated spetra with mean correction factor\n",
    "# create dictionaries to save spectra with activity correction\n",
    "am_s_s={}\n",
    "cs_s_s={}\n",
    "co_s_s={}\n",
    "eu1_s_s={}\n",
    "eu2_s_s={}\n",
    "eu3_s_s={}\n",
    "for clave in claves:\n",
    "    am_s_s[clave]=spec_s_f(am_s_a[clave],factores.iloc[0,5])\n",
    "    eu1_s_s[clave]=spec_s_f(eu1_s_a[clave],factores.iloc[1,5])\n",
    "    eu2_s_s[clave]=spec_s_f(eu2_s_a[clave],factores.iloc[2,5])\n",
    "    eu3_s_s[clave]=spec_s_f(eu3_s_a[clave],factores.iloc[3,5])\n",
    "    cs_s_s[clave]=spec_s_f(cs_s_a[clave],factores.iloc[4,5])\n",
    "    co_s_s[clave]=spec_s_f(co_s_a[clave],factores.iloc[5,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "outputs": [],
   "source": [
    "# determinate the maximum of expertimental spectra and simulated spectra through\n",
    "# ratio among max photo peaks\n",
    "# the maximum of experimental spectra\n",
    "m_am_e=np.array([max_peak(am_e_n[clave],223-20,223+20) for clave in am_e_n])\n",
    "m_eu1_e=np.array([max_peak(eu_e_n[clave],444,473) for clave in eu_e_n])\n",
    "m_eu2_e=np.array([max_peak(eu_e_n[clave],921-20,921+20) for clave in eu_e_n])\n",
    "m_eu3_e=np.array([max_peak(eu_e_n[clave],1297-20,1297+20) for clave in eu_e_n])\n",
    "m_cs_e=np.array([max_peak(cs_e_n[clave],2492-20,2492+20) for clave in cs_e_n])\n",
    "m_co1_e=np.array([max_peak(co_e_n[clave],4000,4500) for clave in co_e_n])\n",
    "m_co2_e=np.array([max_peak(co_e_n[clave],4700,5250) for clave in co_e_n])\n",
    "\n",
    "# the maximum of simulated spectra\n",
    "m_am_s=np.array([max_peak(am_s_s[clave][1],223-20,223+20) for clave in am_s_s])\n",
    "m_eu1_s=np.array([max_peak(eu1_s_s[clave][1],444,473) for clave in eu1_s_s])\n",
    "m_eu2_s=np.array([max_peak(eu2_s_s[clave][1],921-20,921+20) for clave in eu2_s_s])\n",
    "m_eu3_s=np.array([max_peak(eu3_s_s[clave][1],1297-20,1297+20) for clave in eu3_s_s])\n",
    "m_cs_s=np.array([max_peak(cs_s_s[clave][1],2492-20,2492+20) for clave in cs_s_s])\n",
    "m_co1_s=np.array([max_peak(co_s_s[clave][1],4000,4500) for clave in co_s_s])\n",
    "m_co2_s=np.array([max_peak(co_s_s[clave][1],4700,5250) for clave in co_s_s])"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "outputs": [
    {
     "data": {
      "text/plain": "array([[1.05447125e+03, 1.26253939e+02, 3.92883003e+01, 1.08648510e+01,\n        7.13083899e+00],\n       [4.37747465e+02, 4.80622751e+01, 1.54391359e+01, 4.47419856e+00,\n        2.93518629e+00],\n       [4.51900393e+01, 4.57952514e+00, 1.50885756e+00, 4.39447929e-01,\n        2.96706493e-01],\n       [1.04078033e+02, 1.02095252e+01, 3.31730385e+00, 9.73441805e-01,\n        6.29253054e-01],\n       [3.76622404e+01, 3.58835947e+00, 1.17713379e+00, 3.52967538e-01,\n        2.38188363e-01],\n       [3.39449191e+01, 3.65585169e+00, 1.30591411e+00, 4.05222670e-01,\n        3.00820323e-01],\n       [2.99847081e+01, 3.18338683e+00, 1.09887894e+00, 2.91972666e-01,\n        1.82261725e-01]])"
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# matrix of experimental max photopeaks values\n",
    "m_e=np.zeros((7,5))\n",
    "m_e[0,:]=m_am_e\n",
    "m_e[1,:]=m_eu1_e\n",
    "m_e[2,:]=m_eu2_e\n",
    "m_e[3,:]=m_eu3_e\n",
    "m_e[4,:]=m_cs_e\n",
    "m_e[5,:]=m_co1_e\n",
    "m_e[6,:]=m_co2_e\n",
    "\n",
    "# matrix of experimental max photopeaks values\n",
    "m_s=np.zeros((7,5))\n",
    "m_s[0,:]=m_am_s\n",
    "m_s[1,:]=m_eu1_s\n",
    "m_s[2,:]=m_eu2_s\n",
    "m_s[3,:]=m_eu3_s\n",
    "m_s[4,:]=m_cs_s\n",
    "m_s[5,:]=m_co1_s\n",
    "m_s[6,:]=m_co2_s\n",
    "m_s"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [],
   "source": [
    "# estimate the error between simulated and experimental FEPE\n",
    "error=np.zeros((7,5))\n",
    "# determinate FEPE simulated\n",
    "error= np.absolute(m_s-m_e)\n",
    "error=error/m_e*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": "<matplotlib.legend.Legend at 0x1bfdb43e910>"
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": "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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(canal,co_e_n['25cm'], 'xg',label='Exp')\n",
    "plt.plot(canal,co_s_s['25cm'][1],'ob', label='Sim')\n",
    "plt.xlim(4420-20,4420+20)\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x360 with 1 Axes>",
      "image/png": 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\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#plot the experimental, simulated with activity correction, and activity-factor correction\n",
    "fig3=plt.figure(figsize=(6,5))\n",
    "plt.plot(Energy, cs_e_n['5cm'], 'xr',label='Experimental')\n",
    "plt.plot(cs_s_a['5cm'][0], cs_s_a['5cm'][1], '-b',label='Simulated with activity correction')\n",
    "plt.plot(cs_s_s['5cm'][0], cs_s_s['5cm'][1], 'sg',label='Simulated with activity-factor correction')\n",
    "plt.grid()\n",
    "plt.ylabel('Net count rate (counts/s)')\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.xlim(0.5,0.7)\n",
    "#plt.yscale('log')\n",
    "plt.legend(loc='center left')\n",
    "plt.savefig('spectras_com.png', dpi=500)"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "outputs": [
    {
     "data": {
      "text/plain": "                      0 cm       5 cm      10 cm      20 cm      25 cm\nAm-241 59.5 keV  20.813514  21.770746   2.203862  42.253887   2.534236\nEu-152 121 keV   57.373643   3.986132  15.294986  18.291915  19.800610\nEu-152 244 keV   83.814542  11.838019  20.609658  25.235702  26.131164\nEu-152 344 keV   31.957082   1.445718   8.725972   9.854907  14.821922\nCs-137 662 keV   17.511400   1.135944   4.706956   7.653842   6.286546\nCo-60 1173 keV    5.672428   3.387293   7.545904   2.388651   7.649420\nCo-60 1332 keV   15.774799   3.824901   4.491449  13.985139  20.080121",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>0 cm</th>\n      <th>5 cm</th>\n      <th>10 cm</th>\n      <th>20 cm</th>\n      <th>25 cm</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>Am-241 59.5 keV</th>\n      <td>20.813514</td>\n      <td>21.770746</td>\n      <td>2.203862</td>\n      <td>42.253887</td>\n      <td>2.534236</td>\n    </tr>\n    <tr>\n      <th>Eu-152 121 keV</th>\n      <td>57.373643</td>\n      <td>3.986132</td>\n      <td>15.294986</td>\n      <td>18.291915</td>\n      <td>19.800610</td>\n    </tr>\n    <tr>\n      <th>Eu-152 244 keV</th>\n      <td>83.814542</td>\n      <td>11.838019</td>\n      <td>20.609658</td>\n      <td>25.235702</td>\n      <td>26.131164</td>\n    </tr>\n    <tr>\n      <th>Eu-152 344 keV</th>\n      <td>31.957082</td>\n      <td>1.445718</td>\n      <td>8.725972</td>\n      <td>9.854907</td>\n      <td>14.821922</td>\n    </tr>\n    <tr>\n      <th>Cs-137 662 keV</th>\n      <td>17.511400</td>\n      <td>1.135944</td>\n      <td>4.706956</td>\n      <td>7.653842</td>\n      <td>6.286546</td>\n    </tr>\n    <tr>\n      <th>Co-60 1173 keV</th>\n      <td>5.672428</td>\n      <td>3.387293</td>\n      <td>7.545904</td>\n      <td>2.388651</td>\n      <td>7.649420</td>\n    </tr>\n    <tr>\n      <th>Co-60 1332 keV</th>\n      <td>15.774799</td>\n      <td>3.824901</td>\n      <td>4.491449</td>\n      <td>13.985139</td>\n      <td>20.080121</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d_error=pd.DataFrame(error,index=['Am-241 59.5 keV','Eu-152 121 keV','Eu-152 244 keV',\n",
    "                             'Eu-152 344 keV','Cs-137 662 keV','Co-60 1173 keV','Co-60 1332 keV'],\n",
    "                      columns=['0 cm', '5 cm', '10 cm','20 cm', '25 cm'])\n",
    "d_error"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "outputs": [
    {
     "data": {
      "text/plain": "<Figure size 432x288 with 1 Axes>",
      "image/png": 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/c/4kIvcBuSJyNfAC8JuhHFBEZgFXAycAc4BzRGT6UPZlTLzb3dbGpzZs4D7g3Px83iot5eTcXK9jmRjn9uLunSLySeAgoXb+76nq80M8Zgnwuqo2A4jIS4Sakn4yxP0ZE5eeDAS4sqKCpq4uvgH89NhjbXA1ExYSasFxubHIaD44Vk/doA8oUkJoCIiPAi3A34C1qvrVHtstJjT7FwUFBfOXLVtGVgwPMNXY2Gj5PRRL+duA+4DHgGnAd4ExMZS/p1j63fcmlvOXlZWtU9XSnsvd9ur5EnAboUIdxLmBCygebBBVLReRHwPPA43AW0BnL9stBZYClJaWalZWFgsWLBjs4aLG6tWrLb+HYiX/u01NXLRxI+80NfH1CRO4o7iYVJ8vZvL3JpazQ+zn743bXj3fBI5V1UA4DqqqvwV+CyAi/8n/TeJuTEJSVe7dtYsbKisZnZTEX2bP5sz8fK9jmTjltvBXAs3hOqiIjFPVfSIyCbiQULOPMQkp0N7OVRUVrKyt5VN5eTxYUkKBzY5lIsht4b+J0EBta+g2LLOqfm2Ix10uIvlAB3Ctqu4f4n6MiWkv7t/PpeXl1HR0cNfUqVw3YYINrmYizm3hvw94EXibUBv/sKjqx4e7D2NiWUcwyPe2bePH1dXMSE/nqdmzmZdt90SakeG28Heq6g0RTWJMgqhsaeHijRv5V0MDV48fz93TppFps2OZEeS28K9yulc+yQebegbdndOYRPb7PXu4ZssWkkV49Jhj+My4cV5HMgnIbeG/xPl6U7dlQ+rOaUwiOtjZyTWbN/Pwvn2cnJPDQyUlTExL8zqWSVBu79ydEukgxsSr1+vruaS8nOrWVm4rKuLmyZNJsgu4xkP9Fn4ROUVVXxSRC3tbr6orIhPLmNjXpcod1dXc+t57TExL4+V58zgxJ8frWMYMeMb/CUK9ec7tZZ0CVviN6cX7ra1cWl7OS/X1XDRuHPfOmEGOzY5lokS//xNV9Vbn6xdGJo4xsW9FTQ1XVVTQHgzywNFHc1lBgQ2uZqLKQE09/XbhVNW7whvHmNjV3NXF9Vu3snT3bkqzs/lDSQnTMzK8jmXMEQb67HnojpKZwPHAE87zc4GXIxXKmFjzVmMjF23cyKbmZr49cSK3T5nCKJsoxUSpgZp6fgAgIn8FjlPVBuf594FHI57OmCinqvxi506+XVlJfkoKz3/4w5w2ZozXsYzpl9urTZOA9m7P24GisKcxJobsa2/nik2beKaujnPz8/ntzJmMtcHVTAxwW/h/D/xTRB4j1JvnAuB3EUtlTJR7rq6Oy8vLOdDZyT3Tp3NNYaFdwDUxw+0NXEtE5FngJGfRF1R1feRiGROd2oJBbq6q4q4dOzg2I4MX5sxhVozOzmQSl+uOxaq6TkTeB9IARGSSqlZHLJkxUaaiuZmLN25kfWMj1xYW8tOpU0m3wdVMDHI79eJ5wM+AQmAfoTb/TcCxkYtmjLce3ruXW6qqqG5rIy85mYbOTkYnJ7Ny1izO8/u9jmfMkLntb3Y78BFgszNuz2nAPyKWyhiPPbx3L4srKtje1oYCdZ2ddAHfLyqyom9intvC36GqtYBPRHyqugqYG7lYxnhHVflWZSXNwQ/OORQE7nz/fW9CGRNGbtv4D4hIFqGbth4WkX1AZ+RiGTOyOoJBXq6vZ2UgwBOBALvb23vdrrqtrdflxsQSt4V/IdACXA8sAnKA2yIVypiR0NDZybN1dawMBHi6ro4DnZ2k+XycnpdHY1cXtZ1HnttMSk31IKkx4TXQWD2iIU3OoiDwYG/bDOagInI9cBWhewLeJtQ9tHUw+zBmKHa1tfFEIMDK2lpe3L+fdlX8KSmc7/dzvt/PJ/PyyEhKOtzG3725J8PnY0mxzT1kYt9AZ/yrRGQ5sLJ7100RGUWoT//lwCrgAbcHFJGjgK8Bx6hqi4j8CbhoMPswxi1VZWNzMw8B3163jn81NAAwNS2Nrx51FAv9fk7MyTliYpRFBQUAh3v1TEpNZUlx8eHlxsSygQr/GcCVwCMiMgU4AKQTuij8V+BuVX1ziMdNF5EOIAPYNYR9GNOrLlX+4bTXrwwEqGwNfZg8AVgyZQoL/X6OycgY8E7bRQUFVuhNXBK3rTQikgL4gRZVPTCsg4pcBywhdN3gr6q6qJdtFgOLAQoKCuYvW7aMrBi+Q7KxsdHyR1ALsA74O/AacBBIAY4DTgTmNDUxOTPTu4DDFO2///7EcnaI7fxlZWXrVLW05/LB3LnbAewebhARySN0sfjQJ4hHReT/qepDPY63FFgKUFpaqllZWSxYsGC4h/fM6tWrLX+Y7Wtv58naWlYGAjy/fz+twSC5ycmcO2YMC/1+zhgzhmxn1qtozD8YsZw/lrND7OfvjRdzwZ0GvKeqNQAisoLQSdlD/b7KGGBzczMrAwEeDwR47eBBlFBPm8Xjx7PQ7+fjOTmk2Dj4xvTLi8JfDXxERDIIfUI/FVjrQQ4TA4KqrDl4MNReX1vLpuZmAOZlZXFrUREL8/OZk5VlI2MaMwgDFn4RSQKeU9XTwnFAVV0jIn8G3iB0E9h6nCYdYwBau7r424EDPB4I8GQgwN6ODpJF+ERODtcWFnKe38+ktDSvYxoTswYs/KraJSLNIpKjqvXhOKgzifut4diXiQ+1HR087bTXP1dXR1MwSHZSEmc67fVnjRlDbkqK1zGNiQtum3pagbdF5Hng0M1cqOrXIpLKJISqlpbDXS7/Xl9PF1A4ahSXfehDLPT7WZCbS6q11xsTdm4L/9POw5ghU1XWNTSw0jmzf7spdA4xKzOT70yaxEK/n/nZ2fisvd6YiHI7A9eDzt26M5xFFU73TmP61R4Mstppr38iEGBnezs+4KScHH42dSoL/X6mpqd7HdOYhOJ2IpYFhMbo2QYIMFFELlfVlyOWLMF0n/Qj1ocHONDRwTPO4GfP1NVxsKuLDJ+PTznt9WePGYPfJiU3xjNum3p+BpyuqhUAIjIDeASYH6lgiaTngGDb29pYXFEBEDPF//3W1sNdLlcfOECnKuNSUvjs2LEs9Ps5LS/Ppik0Jkq4Lfwph4o+gKpudoZwMGFwS1XVEZN+NAeDfLuykkvGjYvKPuqqyoampsMXZ99obARgZno6N0yYwEK/n38bPfqIwc+MMd5zW/jXishvgd87zxcRGhrFhEFfk3vsam9n2po1nDFmDGeOGUNZXh6ZHp41dwSDvHJospLaWra1tiLAR0aP5o7iYhbm53N0DI+HY0yicFv4/x24ltBwykJoJq5fRSpUopmUmsr2Xop/XnIyx2Zm8sCePfx61y5GiXBybu7hPwQlLkaYHK6Gzk6eq6tjKbD21VfZ39lJqgin5eVxy6RJnJOfz4dschJjYorbwv9lVb0LuOvQAmeEzZ9HJFWCuayggNurqz+wLMPn45fTp7OooIC2YJC/19fzTG0tz9bV8c3KSr5ZWcmk1NTDfwROyctjdHJ4RuDY3dZ2ePCzF5zJSkYD5+fns9Dv5/S8PLLCdCxjzMhz++69nCOL/BW9LDOD1B4MsjwQID8piYzkZHb00qsn1efj1Lw8Ts3L406gurWVZ+vqeLaujkf27WPp7t0ki3BSTs7hPwSzMzNdfxpQVcqdwc9WBgKscSYrmZKWxrXOZCWdb77JqSUlkfo1GGNG0EBTL14MXAJMEZEnuq3KBmojGSxR/Oz999nY3MyTs2Zxjt/v6jWT0tJYXFjI4sJC2oNBXjt48PCnge9UVfGdqioKR406/EfgtLw8nq6r+0B30dunTGFKWhora2t5PBBga0sLAKXZ2dxeVMRCv59Z3f54rI7UL8AYM+IGOuN/ldAY/H5CXToPaQA2RCpUoqhqaeG27du50O93XfR7GuXz8YncXD6Rm8sdU6eyq63t8KeB5TU13L9nD0LowsyhfkPb29q4bNMmAFJEKMvN5YYJEzg3P58JNviZMXGv38KvqtuB7SKyCNh1aEJ0EUkHJhC6ocsMgapy7ZYtJIvw82nTwrbfwtRUrhw/nivHj6czGGRNQwNnbdjAwa6uI7b1Jyez9SMfIcfa641JKG5HwPoT/3fCCNAFPBr+OInj0Zoanq2r44dTpkTsLDvZ5+NjOTk09FL0AWo7O63oG5OA3Bb+ZFVtP/TE+d7uuR+i+s5Ortu6leOysvjKUUdF/HiT+uhu2ddyY0x8c1v4a0TkvENPRGQhEIhMpPh3S1UV+9rbuW/GjBG5s3VJcTEZPYY3zvD5WFJcHPFjG2Oij+t+/MDDInIPoeuE7wOXRSxVHPvnwYP8etcuvnLUUZSOHj0ixzzULTReBoEzxgyP22GZKwnNk5sFiKo2RDZWfOoMBvnS5s2MHzWKH06ZMqLHXlRQYIXeGAMMYrJ1ETkbOBZIO9S3W1Vvi1CuuPTLnTt5s7GRR485Jmx32RpjzGC5auMXkXuBzwNfJdTU81lg8lAOKCIzReTNbo+DIvL1oewrluwDvvvee5w1ZgyfHjvW6zjGmATm9uLuiap6GbBfVX8AfBSYOJQDqmqFqs5V1bmExvNvBh4byr5iyS8J9Yf91fTpUTnMsjEmcbgt/K3O12YRKQQ6gHA0Up8KVDo3isWtJwIB/g58v6iIIptm0BjjMVHVgTcS+S6hk9ZTCQ3HrMBvVPV7wzq4yP3AG6p6Ty/rFgOLAQoKCuYvW7aMrKys4RzOEy2ERrPL6OriN0lJ7i+qRJnGxsaY/P0fYvm9E8vZIbbzl5WVrVPV0iNWqGqfD+Czztcp3ZalAjn9vc7Ng9ANYAGgYKBt58+fr6tWrdJY9I0tW5RVq/SXMZr/kFj9/R9i+b0Ty9lVYzs/sFZ7qakDNfXc5Hxd3u0PRZuq1g/zDxHAmYTO9veGYV9R6a3GRv5rxw4Wjx/PLK/DGGOMY6CWh1oRWcWRwzIDoKrn9fIaty4mNGF7XOpS5UsVFeSnpHBHcTFv7d7tdSRjjAEGLvxnA8cRmmv3ZwNs65qIZACfBL4Urn1Gm6W7drGmoYGHSkrIS7F56Y0x0WOgYZnbgddF5ERVrQnXQVW1GcgP1/6izZ62Nm6qquK0vDwuGTfO6zjGGPMBrrpzhrPoJ4LrKytpDQb5tfXZN8ZEIbf9+I1Lf62r44/79nHz5MlMz8jwOo4xxhzBCn8YtXR18e+bNzMzPZ0bJ03yOo4xxvTK1f1EIjIWuBoo6v4aVb0yMrFi0w+3b6eqtZUX58wh1Wd/U40x0cntjaQrgVeAFwhNu2h62NjUxE/ff5/LCgooy8vzOo4xxvTJbeHPUNUbI5okhgVV+fLmzWQnJXHn1KlexzHGmH65bY94SkTOimiSGPbAnj28Ul/PT6ZOZewom4rYGBPd3Bb+6wgV/1YRaXAeByMZLFbUtLfzrcpKTsrJ4Qsf+pDXcYwxZkBup17MjnSQWPWtykoOdnVx74wZ+KzPvjEmBgxm6sXzgJOdp6tV9anIRIodq/fv58G9e7lp0iSOzcz0Oo4xxrjidurFOwg192x0Htc5yxJWWzDIlzdvZkpaGv8xeUizUBpjjCfcnvGfBcxV1SCAiDwIrAe+E6lg0e4n1dVUtLTwzOzZZCQleR3HGGNcG8xdRrndvs8Jc46YsqW5mSXbt/P5sWM5Iz9ux5ozxsQpt2f8PwLWO2PzC6G2/pv6f0l8UlWu2bKFVJ+Pu6dN8zqOMcYMmttePY+IyGrgeEKF/0ZV3RPJYNHqkX37eGH/fn41fTrjU1O9jmOMMYPWb1OPiBztfD0OGA/sAN4HCp1lCWV/RwfXb93KCdnZfKmw0Os4xhgzJAOd8d8ALKb32bcUOCXsiaLYTVVV1HZ08NyHP0yS9dk3xsSogWbgWux8e6aqtnZfJyJpEUsVhV6rr+e+3bu5YcIE5mbb/WzGmNjltlfPqy6XxaWOYJAvbd7MxNRUflBU5HUcY4wZln7P+EXkQ8BRQLqIzCN0YRdgNDDk6aVEJBdYBswi1GR0paq+NtT9Rdp/7djB201NPD5rFlnJrm92NsaYqDRQFfsUcAUwgVA7/6HCfxC4eRjH/TnwrKp+RkRGMYw/IpG2vbWV72/bxsL8fBb6/V7HMcaYYRuojf9B4EER+bSqLg/HAUVkNKH7AK5wjtEOtIdj3+GmqnxlyxYE+OX06V7HMcaYsBBVHXgjkf8EfqKqB5znecA3VPU/Bn1AkbnAUkJj/swB1gHXqWpTj+0WE+pRREFBwfxly5aRlZU12MMNyQuE2qH2Os9PBQb9g/bQ2Ng4YvkjwfJ7K5bzx3J2iO38ZWVl61S19IgVqjrgA1jfy7I33Ly2l9eVAp3AvznPfw7c3t9r5s+fr6tWrdKR8NCePZrx0kvKqlWHHxkvvaQP7dkzrP2OVP5IsfzeiuX8sZxdNbbzA2u1l5rqtldPkogcvk1VRNKBod62ugPYoaprnOd/BqLmZrBbqqpoDgY/sKw5GOSWqiqPEhljTHi57aLyEPA3EfkfnF44wINDOaCq7hGR90VkpqpWEGpJ2TiUfUVCdVvboJYbY0yscTtWz09EZANwGqGePber6nPDOO5XgYedHj1VwBeGsa+wmpSayvZeivwkG5fHGBMnBtMpvRzoVNUXRCRDRLJVtWEoB1XVNwm19UedJcXFXF5eTle3ZRk+H0uKiz3LZIwx4eR2Bq6rCbXF3+csOgp4PEKZPPXZsWMZJUKmz4cAk1NTWTpzJosKCryOZowxYeH2jP9a4ARgDYCqbhGRcRFL5aEX9++nRZWnZs3ibJtkxRgTh9z26mnT0I1WAIhIMqGLvHFneSBAdlISp+XleR3FGGMiwm3hf0lEbiY0Zs8ngUeBJyMXyxtdqjweCHBOfj6pvsHMSmmMMbHDbXW7EagB3ga+BPyF4d/MGnVeOXCAQEcHF9qYPMaYODZgG7+I+IANqjoL+E3kI3lnRSBAms/Hmda2b4yJYwOe8atqEHhLRCaNQB7PBFVZUVPDGWPGkJmU5HUcY4yJGLe9esYD74rIP4HDg6mp6nkRSeWBfzU0sLO9nR9ZM48xJs65Lfw/iGiKKLC8poZkEc61Zh5jTJxz28b/K6eNPy6p08xzam4uuSkpXscxxpiIsjZ+YENTE5WtrXx67FivoxhjTMRZGz+woqYGH9jUisaYhGBt/ITa9z+ek8O4UaO8jmKMMRHn6gYuVX0J2ARkO49yZ1nMq2hu5t3mZi60Zh5jTIJwOzrn54B/Ap8FPgesEZHPRDLYSHmspgaAC6yZxxiTINw29dwCHK+q+wBEZCyhOcn/HKlgI2V5IMAJ2dlMTEvzOooxxowIt2P1+A4VfUftIF4btapbW1nb0GDNPMaYhOL2jP9ZEXkOeMR5/nngmchEGjkrnGYeG5TNGJNI3M65+y0RuRA4idCcu0tV9bGIJhsBKwIBZmdmMj0jw+soxhgzYvot/CIyDShQ1X+o6gpghbP8ZBGZqqqVQzmoiGwDGoAuQvP4jvj8u3va2vh7fT23FhWN9KGNMcZTA7XT/xehAt1Ts7NuOMpUda4XRR9gZW0tijXzGGMSz0CFv0hVN/RcqKprgaKIJBohy2tqmJ6ezqzMTK+jGGPMiBLVvqfOFZGtqjptsOsGPKjIe8B+QvP23qeqS3vZZjGwGKCgoGD+smXLyMrKGsrhjnAQuJDQDQmLw7LHgTU2NoYtvxcsv7diOX8sZ4fYzl9WVrau11YVVe3zQagXz9W9LP8i8L/9vXaA/RY6X8cBbwEn97f9/PnzddWqVRouD+zeraxapf+srw/bPgcSzvxesPzeiuX8sZxdNbbzA2u1l5o6UK+erwOPicgiYJ2zrBQYBVww1L9CqrrL+bpPRB4DTgBeHur+BmtFTQ0TU1Mpzc4eqUMaY0zU6Lfwq+pe4EQRKQMOjcf/tKq+ONQDikgmoRvCGpzvTwduG+r+Bquhs5Pn6ur4cmEhIjJShzXGmKjhth//KmBVmI5ZQOhTxKHj/0FVnw3Tvgf0TF0dbap2t64xJmG5vXM3bFS1Cpgz0sc9ZHlNDeNSUvhYTo5XEYwxxlMxP97OYLR2dfF0bS3n+/0kWTOPMSZBJVTh/+v+/TQFg9bMY4xJaAlV+FfU1JCbnExZbq7XUYwxxjMJU/g7gkGeqK3l3Px8RvkS5sc2xpgjJEwFXH3gAPs7O/m0NfMYYxJcwhT+FYEAmT4fp+fleR3FGGM8lRCFv0uVx2pqOCs/n/SkJK/jGGOMpxKi8L9WX8/ejg4bgtkYY0iQwr88EGCUCGfl53sdxRhjPBf3hV9VWVFTw+ljxjA6ecRvVDbGmKgT94V/XUMD1W1t1sxjjDGOuC/8KwIBkoDzrPAbYwwQx4X/4b17mfzaa/youpoUEZ6tq/M6kjHGRIW4bPR+eO9eFldU0BwMAtCqyuKKCgAWFRR4Gc0YYzwXl2f8t1RVHS76hzQHg9xSVeVRImOMiR5xWfir29oGtdwYYxJJXBb+Sampg1pujDGJJC4L/5LiYjJ6jMCZ4fOxpLjYo0TGGBM94rLwLyooYOnMmUxOTUWAyampLJ050y7sGmMMHvbqEZEkYC2wU1XPCff+FxUUWKE3xpheeHnGfx1Q7uHxjTEmIXlS+EVkAnA2sMyL4xtjTCITVR35g4r8GfgRkA18s7emHhFZDCwGKCgomL9s2TKysrJGNmgYNTY2Wn4PWX7vxHJ2iO38ZWVl61S1tOfyEW/jF5FzgH2quk5EFvS1naouBZYClJaWalZWFgsW9Ll51Fu9erXl95Dl904sZ4fYz98bL5p6PgacJyLbgD8Cp4jIQx7kMMaYhORJU8/hg4fO+Htt6umxXQ3QBARGIFak+LH8XrL83onl7BDb+Ser6tieC2NikDZVHSsia3trq4oVlt9blt87sZwdYj9/bzwt/Kq6GljtZQZjjEk0cXnnrjHGmL7FUuFf6nWAYbL83rL83onl7BD7+Y/g6cVdY4wxIy+WzviNMcaEgRV+Y4xJMFFX+EXkDBGpEJGtIvKdXtaLiPzCWb9BRI7zImdvXGRf5GTeICKvisgcL3L2ZaD83bY7XkS6ROQzI5lvIG7yi8gCEXlTRN4VkZdGOmN/XPz/yRGRJ0XkLSf/F7zI2RcRuV9E9onIO32sj9r3LrjKH9Xv30FR1ah5AElAJVAMjALeAo7psc1ZwDOAAB8B1nidexDZTwTynO/PjJbsbvN32+5F4C/AZ7zOPcjffy6wEZjkPB/nde5B5r8Z+LHz/VigDhjldfZu+U4GjgPe6WN9VL53B5E/at+/g31E2xn/CcBWVa1S1XZCQzos7LHNQuB3GvI6kCsi40c6aC8GzK6qr6rqfufp68CEEc7YHze/e4CvAsuBfSMZzgU3+S8BVqhqNYCqRtPP4Ca/AtkiIkAWocLfObIx+6aqLxPK1Jdofe8CA+eP8vfvoERb4T8KeL/b8x3OssFu44XB5voiobOfaDFgfhE5CrgAuHcEc7nl5vc/A8gTkdUisk5ELhuxdANzk/8eoATYBbwNXKeqwZGJFxbR+t4dimh7/w5KtA3ZIL0s69nf1M02XnCdS0TKCP3HOSmiiQbHTf7/Am5U1a7QSWdUcZM/GZgPnAqkA6+JyOuqujnS4Vxwk/9TwJvAKcBU4HkReUVVD0Y4W7hE63t3UKL0/Tso0Vb4dwATuz2fQOjsZrDbeMFVLhH5MKEJaM5U1doRyuaGm/ylwB+dou8HzhKRTlV9fEQS9s/t/52AqjYBTSLyMjAHiIbC7yb/F4A7NNTIvFVE3gOOBv45MhGHLVrfu65F8ft3UKKtqedfwHQRmSIio4CLgCd6bPMEcJnTQ+AjQL2q7h7poL0YMLuITAJWAJdGyVlmdwPmV9UpqlqkqkXAn4FroqTog7v/OyuBj4tIsohkAP9G9Ez/6SZ/NaFPK4hIATATqBrRlMMTre9dV6L8/TsoUXXGr6qdIvIV4DlCvRzuV9V3ReTLzvp7CfUmOQvYCjQTOgvynMvs3wPygV87Z82dGiWj/rnMH7Xc5FfVchF5FtgABIFlqtpr172R5vL3fzvwgIi8TajZ5EZVjZrhgkXkEWAB4BeRHcCtQApE93v3EBf5o/b9O1g2ZIMxxiSYaGvqMcYYE2FW+I0xJsFY4TfGmARjhd8YYxKMFX5jjEkwVvhNTHNGCX2z26PPUUVHktNX/UURGe08VxH5fbf1ySJSIyJP9bOPTBGpFZGcHssfF5HPicg5IvKDyP0UJl5Z4TexrkVV53Z73DHcHYpIOO5vOQt4q9twCk3ALBFJd55/EtjZ3w6cO4z/CpzfLVsOoaECngKeBs5zbkYzxjUr/CYuicg2EfmBiLwhIm+LyNHO8kxn3PV/ich6EVnoLL9CRB4VkSeBv4pIhoj8yRl7/X9FZI2IlIrIF0Xk7m7HuVpE7uolwiJCdwp39wxwtvP9xcAj3fbTay5nm4u67eMC4FlVbXaGblgNnDPU35NJTFb4TaxL79HU8/lu6wKqehzw38A3nWW3AC+q6vFAGfBTEcl01n0UuFxVTwGuAfar6ocJ3TE739nmj4TOslOc518A/qeXXB8D1vVY9kfgIhFJAz4MrOm2rq9czwLzRSTf2e4iuv3BANYCH+/zt2NML6JqyAZjhqBFVef2sW6F83UdcKHz/emECvehPwRpwCTn++dV9dB47CcBPwdQ1XdEZIPzfZOIvAicIyLlQIqqvt3LsceoakP3Baq6QUSKCJ3t/6XH9r3mcoaZeAL4jIgsB+YSav45ZB9Q2MfPb0yvrPCbeNbmfO3i//6vC/BpVa3ovqGI/Buhdni6bdeXZYRmw9pE72f7AJ0i4utlvPwngDsJjQmT3215r7kcjwD/4WyzUlU7uq1LA1r6yWrMEaypxySa54CvijPKlojM62O7vwOfc7Y5Bph9aIWqriE0vPAlfLDZpbsKQtMo9nQ/cFsvnxL6y7UKmA5c28vxZgBRMdCciR1W+E2s69nGP1CvntsJjbi4QUKTat/ex3a/BsY6TTw3EhrRs77b+j8B/+g2FV9PTxM6q/8AVd2hqj8fTC7nU8NyQp8QXu7xujLnWMa4ZqNzGtMLEUki1H7fKiJTgb8BM5z5cHH639+tqn/r4/XjCc0v+8kIZiwA/qCqp0bqGCY+WRu/Mb3LAFY5vXcE+HdVbReRXEIzXr3VV9EHUNXdIvIbERkdwakRJwHfiNC+TRyzM35jjEkw1sZvjDEJxgq/McYkGCv8xhiTYKzwG2NMgrHCb4wxCeb/AzZmtxJQSVtQAAAAAElFTkSuQmCC\n"
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(energia_,factores['Mean'], 'o-c')\n",
    "plt.xlabel('Energy (MeV)')\n",
    "plt.ylabel('Correction factor (adimentional)')\n",
    "plt.grid()\n",
    "plt.savefig('correction_f_r.jpg', dpi=500)\n",
    "\n"
   ],
   "metadata": {
    "collapsed": false,
    "pycharm": {
     "name": "#%%\n"
    }
   }
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}